Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication
Abstract Federated learning (FL) is a distributed learning paradigm that enables model training while protecting user privacy. However, frequent communication between the server and clients also provides opportunities for attackers to intercept or tamper with parameters, thereby affecting the global...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | Journal of Big Data |
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| Online Access: | https://doi.org/10.1186/s40537-025-01165-y |
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| author | Xiaomeng Li Yanjun Li Hui Wan Cong Wang |
| author_facet | Xiaomeng Li Yanjun Li Hui Wan Cong Wang |
| author_sort | Xiaomeng Li |
| collection | DOAJ |
| description | Abstract Federated learning (FL) is a distributed learning paradigm that enables model training while protecting user privacy. However, frequent communication between the server and clients also provides opportunities for attackers to intercept or tamper with parameters, thereby affecting the global model’s performance. To enhance the robustness of FL against attackers, we propose a framework called Byzantine-robust federated learning by adaptive tripartite authentication (BRFLATA). Specifically, BRFLATA consists of four modules: (1) adaptive client matching mechanism, (2) client authentication, (3) reliable communication link, and (4) global model update through an incentive mechanism. Through these dedicated settings, BRFLATA can authenticate each client, detect potential Byzantine clients and link attackers, and mitigate their impact on the global model’s performance by adjusting the clients’ weights during global model aggregation. We have validated the effectiveness of our proposed method through extensive experiments on widely used datasets across multiple scenarios, comparing it with state-of-the-art methods. |
| format | Article |
| id | doaj-art-9f902492f3fb4b41809affb38db95a46 |
| institution | Kabale University |
| issn | 2196-1115 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj-art-9f902492f3fb4b41809affb38db95a462025-08-20T03:53:57ZengSpringerOpenJournal of Big Data2196-11152025-05-0112112110.1186/s40537-025-01165-yEnhancing Byzantine robustness of federated learning via tripartite adaptive authenticationXiaomeng Li0Yanjun Li1Hui Wan2Cong Wang3The Future Laboratory, Tsinghua UniversityThe College of Communication Engineering, Jilin UniversityThe College of Communication Engineering, Jilin UniversityThe College of Communication Engineering, Jilin UniversityAbstract Federated learning (FL) is a distributed learning paradigm that enables model training while protecting user privacy. However, frequent communication between the server and clients also provides opportunities for attackers to intercept or tamper with parameters, thereby affecting the global model’s performance. To enhance the robustness of FL against attackers, we propose a framework called Byzantine-robust federated learning by adaptive tripartite authentication (BRFLATA). Specifically, BRFLATA consists of four modules: (1) adaptive client matching mechanism, (2) client authentication, (3) reliable communication link, and (4) global model update through an incentive mechanism. Through these dedicated settings, BRFLATA can authenticate each client, detect potential Byzantine clients and link attackers, and mitigate their impact on the global model’s performance by adjusting the clients’ weights during global model aggregation. We have validated the effectiveness of our proposed method through extensive experiments on widely used datasets across multiple scenarios, comparing it with state-of-the-art methods.https://doi.org/10.1186/s40537-025-01165-yFederated learningAdaptive matchingByzantine robustnessCredibilityParameter authenticationReliable communication link |
| spellingShingle | Xiaomeng Li Yanjun Li Hui Wan Cong Wang Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication Journal of Big Data Federated learning Adaptive matching Byzantine robustness Credibility Parameter authentication Reliable communication link |
| title | Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication |
| title_full | Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication |
| title_fullStr | Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication |
| title_full_unstemmed | Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication |
| title_short | Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication |
| title_sort | enhancing byzantine robustness of federated learning via tripartite adaptive authentication |
| topic | Federated learning Adaptive matching Byzantine robustness Credibility Parameter authentication Reliable communication link |
| url | https://doi.org/10.1186/s40537-025-01165-y |
| work_keys_str_mv | AT xiaomengli enhancingbyzantinerobustnessoffederatedlearningviatripartiteadaptiveauthentication AT yanjunli enhancingbyzantinerobustnessoffederatedlearningviatripartiteadaptiveauthentication AT huiwan enhancingbyzantinerobustnessoffederatedlearningviatripartiteadaptiveauthentication AT congwang enhancingbyzantinerobustnessoffederatedlearningviatripartiteadaptiveauthentication |